The accurate reconstruction of three-dimensional shapes from imagery is a quickly advancing field of computer vision. A basic concept of three-dimensional reconstruction algorithms is to find correspondences (e.g. matched pixels) among two-dimensional posed images. The correspondences can then be used to assist in reconstructing three-dimensional models of the shapes.
However, it is well known that the three-dimensional reconstruction of the surface of a body of water (e.g. a river, lake, or ocean) poses an extreme challenge to current reconstruction algorithms. In particular, because the surface of the water is textureless and constantly moving, established pixel correspondences are subject large errors. Therefore, reconstructed water surfaces generally exhibit a large degree of noise and appear spiky or otherwise poorly reconstructed.
One solution to address this problem is the use of a manually-generated water mask that identifies locations at which bodies of water reside. The locations covered by the water mask are not reconstructed, but instead smooth hole filling is performed. However, manual creation of a highly accurate water mask is a time consuming process and, therefore, water masks tend to be imprecise in nature. In particular, use of an imprecise water mask can cause bridges to be treated as water and therefore cause the bridges to fail to be reconstructed.
Another potential solution to the above noted challenges is to intersect the water mask with a road mask that identifies locations at which roads reside, thereby generating a bridge mask indicating the locations of bridges. However, the road mask may not contain road width information or may, similar to the water mask, contain various inaccuracies. Therefore, complete reliance upon such inaccurate road and water masks can cause various defects in the corresponding bridge mask and, ultimately, the resulting reconstructions.
However, bridges can be landmarks or otherwise convey the character of a city. For example, the Brooklyn Bridge remains an enduring icon of New York City. Therefore, failure to reconstruct bridges can greatly diminish the realism, consistency, and user experience of exploring a rendering of a three-dimensional model of a city.
Furthermore, similar to other structures, bridges can generally be reconstructed accurately using reconstruction algorithms. Therefore, given accurate and precise masking, imagery of bridges can be used to successfully reconstruct bridges and contribute to the realism and consistency of a three-dimensional model of a city or the entire Earth.
Thus, systems and methods for identifying images depicting bridges and then determining corresponding bridge boundaries for the identified bridges are desirable. In particular, knowledge of bridge boundaries within an image can be used to provide an improved bridge mask for use in association with three-dimensional reconstructions.